Control System Design Guide (eBook)
464 Seiten
Elsevier Science (Verlag)
978-0-08-047013-9 (ISBN)
* Teaches controls with an intuitive approach, avoiding unnecessary mathematics.
* Key topics are demonstrated with realistic models of control systems.
* All models written in Visual ModelQ, a full graphical simulation environment available freely via the internet.
* New material on OBSERVERS explained using practical applications.
* Explains how to model machines and processes, including how to measure working equipment, describes many nonlinear behaviours seen in industrial control systems.
* Electronic motion control, including details of how motors and motor feedback devices work, causes and cures of mechanical resonance, and how position loops work.
Control System Design Guide, 3E will help engineers to apply control theory to practical systems using their PC. This book provides an intuitive approach to controls, avoiding unnecessary mathematics and emphasizing key concepts with more than a dozen control system models. Whether readers are just starting to use controllers or have years of experience, this book will help them improve their machines and processes. - Teaches controls with an intuitive approach, avoiding unnecessary mathematics- Key topics are demonstrated with realistic models of control systems- All models written in Visual ModelQ, a full graphical simulation environment available freely via the internet- New material on OBSERVERS explained using practical applications- Explains how to model machines and processes, including how to measure working equipment; describes many nonlinear behaviours seen in industrial control systems- Electronic motion control, including details of how motors and motor feedback devices work, causes and cures of mechanical resonance, and how position loops work
Cover 1
Frontmatter 2
Half Title Page 2
Title Page 4
Copyright 5
Dedication Page 6
Contents 8
Preface 22
Section I: Applied Principles of Controls 26
Important Safety Guidelines for Readers 28
1. Introduction to Controls 30
1.1 Visual ModelQ Simulation Environment 31
1.2 The Control System 32
1.3 The Controls Engineer 33
2. The Frequency Domain 36
2.1 The Laplace Transform 36
2.2 Transfer Functions 37
2.3 Examples of Transfer Functions 39
2.4 Block Diagrams 43
2.5 Phase and Gain 47
2.6 Measuring Performance 50
2.7 Questions 54
3. Tuning a Control System 56
3.1 Closing Loops 56
3.2 A Detailed Review of the Model 59
3.3 The Open-Loop Method 64
3.4 Margins of Stability 65
3.5 A Zone-Based Tuning Procedure 70
3.6 Variation in Plant Gain 73
3.7 Multiple (Cascaded) Loops 75
3.8 Saturation and Synchronization 76
3.9 Questions 79
4. Delay in Digital Controllers 82
4.1 How Sampling Works 82
4.2 Sources of Delay in Digital Systems 83
4.3 Experiment 4A: Understanding Delay in Digital Control 86
4.4 Selecting the Sample Time 89
4.5 Questions 92
5. The z-Domain 94
5.1 Introduction to the z-Domain 94
5.2 z Phasors 96
5.3 Aliasing 98
5.4 Experiment 5A: Aliasing 99
5.5 From Transfer Function to Algorithm 101
5.6 Functions for Digital Systems 103
5.7 Reducing the Calculation Delay 112
5.8 Selecting a Processor 113
5.9 Quantization 116
5.10 Questions 119
6. Six Types of Controllers 122
6.1 Tuning in This Chapter 123
6.2 Using the Proportional Gain 123
6.3 Using the Integral Gain 127
6.4 Using the Differential Gain 136
6.5 PID+ Control 143
6.6 PD Control 146
6.7 Choosing the Controller 149
6.8 Experiments 6A–6F 149
6.9 Questions 150
7. Disturbance Response 152
7.1 Disturbances 153
7.2 Disturbance Response of a Velocity Controller 159
7.3 Disturbance Decoupling 165
7.4 Questions 174
8. Feed-Forward 176
8.1 Plant-Based Feed-Forward 176
8.2 Feed-Forward and the Power Converter 179
8.3 Delaying the Command Signal 185
8.4 Variation in Plant and Power Converter Operation 190
8.5 Feed-Forward for the Double-Integrating Plant 192
8.6 Questions 193
9. Filters in Control Systems 196
9.1 Filters in Control Systems 196
9.2 Filter Passband 200
9.3 Implementation of Filters 208
9.4 Questions 213
10. Introduction to Observers in Control Systems 216
10.1 Overview of Observers 216
10.2 Experiments 10A–10C: Enhancing Stability with an Observer 221
10.3 Filter Form of the Luenberger Observer 226
10.4 Designing a Luenberger Observer 230
10.5 Introduction to Tuning an Observer Compensator 237
10.6 Questions 242
Section II: Modeling 244
11. Introduction to Modeling 246
11.1 What Is a Model? 246
11.2 Frequency-Domain Modeling 247
11.3 Time-Domain Modeling 249
11.4 Questions 263
12. Nonlinear Behavior and Time Variation 264
12.1 LTI Versus non-LTI 264
12.2 Non-LTI Behavior 265
12.3 Dealing with Nonlinear Behavior 267
12.4 Ten Examples of Nonlinear Behavior 270
12.5 Questions 286
13. Seven Steps to Developing a Model 288
13.1 Determine the Purpose of the Model 288
13.2 Model in SI Units 290
13.3 Identify the System 291
13.4 Build the Block Diagram 294
13.5 Select Frequency or Time Domain 295
13.6 Write the Model Equations 295
13.7 Verify the Model 295
Section III: Motion Control 298
14. Encoders and Resolvers 300
14.1 Accuracy, Resolution, and Response 302
14.2 Encoders 302
14.3 Resolvers 303
14.4 Position Resolution, Velocity Estimation, and Noise 308
14.5 Alternatives for Increasing Resolution 312
14.6 Cyclic Error and Torque/Velocity Ripple 314
14.7 Experiment 14B: Cyclical Errors and Torque Ripple 319
14.8 Choosing a Feedback Device 323
14.9 Questions 325
15. Basics of the Electric Servomotor and Drive 328
15.1 Definition of a Drive 329
15.2 Definition of a Servo System 330
15.3 Basic Magnetics 330
15.4 Electric Servomotors 335
15.5 Permanent-Magnet (PM) Brush Motors 338
15.6 Brushless PM Motors 347
15.7 Six-Step Control of Brushless PM Motor 360
15.8 Induction and Reluctance Motors 362
15.9 Questions 364
16. Compliance and Resonance 366
16.1 Equations of Resonance 368
16.2 Tuned Resonance vs. Inertial-Reduction Instability 370
16.3 Curing Resonance 375
16.4 Questions 385
17. Position-Control Loops 388
17.1 P/PI Position Control 388
17.2 PI/P Position Control 399
17.3 PID Position Control 400
17.4 Comparison of Position Loops 405
17.5 Bode Plots for Positioning Systems 409
17.6 Questions 412
18. Using the Luenberger Observer in Motion Control 414
18.1 Applications Likely to Benefit from Observers 414
18.2 Observing Velocity to Reduce Phase Lag 416
18.3 Acceleration Feedback 431
18.4 Questions 435
Appendix A: Active Analog Implementation of Controller Elements 438
Integrator 438
Differentiator 439
Lag Compensator 439
Lead Compensator 440
Lead-Lag Compensator 441
Sallen-and-Key Low-Pass Filter 441
Adjustable Notch Filter 442
Appendix B: European Symbols for Block Diagrams 444
Part I. Linear Functions 444
Part II. Nonlinear Functions 445
Appendix C: The Runge–Kutta Method 448
The Runge–Kutta Algorithm 448
Basic Version of the Runge–Kutta Algorithm 449
C Programming Language Version of the Runge–Kutta Algorithm 451
H-File for C Programming Language Version 452
Appendix D: Development of the Bilinear Transformation 454
Bilinear Transformation 454
Prewarping 454
Factoring Polynomials 455
Phase Advancing 456
Appendix E: The Parallel Form of Digital Algorithms 458
Appendix F: Basic Matrix Math 462
Matrix Summation 462
Matrix Multiplication 462
Matrix Scaling 463
Matrix Inversion 463
Appendix G: Answers to End–of–Chapter Questions 464
Chapter 2 464
Chapter 3 464
Chapter 4 465
Chapter 5 465
Chapter 6 466
Chapter 7 466
Chapter 8 467
Chapter 9 467
Chapter 10 468
Chapter 11 468
Chapter 12 470
Chapter 14 470
Chapter 15 471
Chapter 16 472
Chapter 17 472
Chapter 18 473
References 476
Index 482
Chapter 1 Introduction to Controls
Control theory is used for analysis and design of feedback systems, such as those that regulate temperature, fluid flow, motion, force, voltage, pressure, tension, and current. Skillfully used, control theory can guide engineers in every phase of the product and process design cycle. It can help engineers predict performance, anticipate problems, and provide solutions.
Colleges teach controls with little emphasis on day-to-day problems. The academic community focuses on mathematical derivations and on the development of advanced control schemes; it often neglects the methods that are commonly applied in industry. Students can complete engineering programs that include courses on controls and still remain untutored on how to design, model, build, tune, and troubleshoot a basic control system. The unfortunate result is that many working engineers lay aside analysis when they practice their profession, relying instead on company history and trial-and-error methods.
This book avoids the material and organization of most control theory textbooks. For example, design guidelines are presented throughout; these guidelines are a combination of industry-accepted practices and warnings against common pitfalls. Nontraditional subjects, such as filters and modeling, are presented here because they are essential to understanding and implementing control systems in the workplace. The focus of each chapter is to teach how to use controls to improve a working machine or process.
The wide availability of personal computers and workstations is an important advance for control system designers. Many of the classical control methods, such as the root locus method, are graphical rather than analytical. Their creators sought to avoid what was then the overwhelming number of computations required for analytical methods. Fortunately, these calculations no longer present a barrier. Virtually every personal computer can execute the calculations required by analytical methods. With this in mind, the principles and methods presented herein are essentially analytical, and the arithmetic is meant to be carried out by a computer.
1.1 Visual ModelQ Simulation Environment
Most engineers understand the foundations of control theory. Concepts such as transfer functions, block diagrams, the s-domain, and Bode plots are familiar to most of us. But how should working engineers apply these concepts? As in most disciplines, they must develop intuition, and this requires fluency in the basics. In order to be fluent, you must practice.
When studying control system techniques, finding equipment to practice on is often difficult. As a result, designers often rely on computer simulations. To this end, the author developed, as a companion to this book, Visual ModelQ, a stand-alone, graphical, PC-based simulation environment. The environment provides time-domain and frequency-domain analysis of analog and digital control systems. Visual ModelQ is an enhancement of the original ModelQ, in that Visual ModelQ allows readers to view and build models graphically. Dozens of Visual ModelQ models were developed for this book. These models are used extensively in the chapters that follow. Readers can run these experiments to verify results and then modify parameters and other conditions to experiment with the concepts of control systems.
Visual ModelQ is written to teach control theory. It makes convenient those activities that are necessary for studying controls. Control law gains are easy to change. Plots of frequency-domain response (Bode plots) are run with the press of a button. The models in Visual ModelQ run continuously, just as real-time controllers do. The measurement equipment runs independently, so you can change parameters and see the effects immediately.
1.1.1 Installation of Visual ModelQ
Visual ModelQ is available at www.qxdesign.com. The unregistered version is available free of charge. Although the unregistered version lacks several features, it can execute all the models used in this book. Readers may elect to register their copies of Visual ModelQ at any time; see www.qxdesign.com for details.
Visual ModelQ runs on PCs using Windows 95, Windows 98, Windows 2000, Windows NT, and Windows XP. Download and run the executable file setup.exe for Visual ModelQ V6.0 or later. Visual ModelQ installs with both a User’s Manual and a Reference Manual. After installation, read the User’s Manual. Note that you can access the Reference Manual via Internet Explorer by pressing the Fl key. Finally, check the Web site from time to time for updated software.
1.1.2 Errata
Check www.qxdesign.com for errata. It is the author’s intention to regularly update the Web page as corrections become known.
1.2 The Control System
The general control system, as shown in Figure 1-1, can be divided into the controller and the machine. The controller can be divided into the control laws and the power converter. The machine may be a temperature bath, a motor, or, as in the case of a power supply, an inductor/capacitor circuit. The machine can also be divided into two parts: the plant and the feedback device(s). The plant receives two types of signals: a controller output from the power converter and one or more disturbances. Simply put, the goal of the control system is to drive the plant in response to the command while overcoming disturbances.
1.2.1 The Controller
The controller incorporates both control laws and power conversion. Control laws, such as proportional-integral-differential (PID) control, are familiar to control engineers. The process of tuning — setting gains to attain desired performance — amounts to adjusting the parameters of the control laws. Most controllers let designers adjust gains; the most flexible controllers allow the designer to modify the control laws themselves. When tuning, most control engineers focus on attaining a quick, stable command response. However, in some applications, rejecting disturbances is more important than responding to commands. All control systems should demonstrate robust performance because even nearly identical machines and processes vary somewhat from one to the other, and they change over time. Robust operation means control laws must be designed with enough margin to accommodate reasonable changes in the plant and power converter.
Virtually all controllers have power converters. The control laws produce information, but power must be applied to control the plant. The power converter can be driven by any available power source, including electric, pneumatic, hydraulic, or chemical power.
Figure 1-1. The general control system.
1.2.2 The Machine
The machine is made of two parts: the plant and the feedback. The plant is the element or elements that produce the system response. Plants are generally passive, and they usually dissipate power. Examples of plants include a heating element and a motor coupled to its load.
Control systems need feedback because the plant is rarely predictable enough to be controlled open loop — that is, without feedback. This is because most plants integrate the power converter output to produce the system response. Voltage is applied to inductors to produce current; torque is applied to inertia to produce velocity; pressure is applied to produce fluid flow. In all these cases, the control system cannot control the output variable directly but must provide power to the machine as physics allows and then monitor the feedback to ensure that the plant is on track.
1.3 The Controls Engineer
The focal task of many controls engineers is system integration and commissioning. The most familiar part of this process is tuning the control loops. This process can be intimidating. Often dozens of parameters must be fine-tuned to ensure that the system lives up to the specification. Sometimes that specification is entirely formal, but more often it is a combination of formal requirements and know-how gained with years of experience. Usually only the most senior engineers in a company are capable of judging when a system is performing well enough.
For some control systems, each installation may require days or weeks to be correctly commissioned. In a complex machine such as a rolling mill, that process can take months. Each piece of the machine must be carefully tuned at the site. So even after the design of the machine is complete, the expertise of a controls engineer is required each time a unit is installed.
Although most controls engineers focus on installation, their job should begin when the machine is designed. Many companies fail to take advantage of their controls expertise early in a project; this is shortsighted. A controls engineer may suggest an improved feedback device or enhancements to a machine that will help overcome a stubborn problem. Ideally, the project manager will solicit this input early, because changes of this nature are often difficult to make later.
The controls engineer should also contribute to the selection of the controller. There are many controls-oriented factors that should be taken into account. Does the controller implement familiar control laws? For digital controllers, is the processor fast enough for the needs of the application? Is the unit appropriate for the support team and for the customer base? The selection and specification of a controller often involve input from many people, but some questions can be answered best by a...
Erscheint lt. Verlag | 30.4.2004 |
---|---|
Sprache | englisch |
Themenwelt | Sachbuch/Ratgeber |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Informatik ► Weitere Themen ► CAD-Programme | |
Technik ► Bauwesen | |
Technik ► Elektrotechnik / Energietechnik | |
Technik ► Maschinenbau | |
ISBN-10 | 0-08-047013-0 / 0080470130 |
ISBN-13 | 978-0-08-047013-9 / 9780080470139 |
Haben Sie eine Frage zum Produkt? |
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